Building a Credible AI Engineering Portfolio

An AI portfolio has to do more than list models and frameworks. Recruiters and engineers look for evidence that the work was real, the system had constraints, and the builder made technical decisions intentionally.

What I want each project to prove

Each project card should answer four questions quickly:

• What problem did this solve?
• What did I personally build?
• What technical choices made it work?
• Where is the proof?

That proof can be a live deployment, a GitHub repository, an architecture diagram, a screenshot, a short demo video, a benchmark, or a write-up about a difficult technical decision.

Why case studies matter

Short cards are useful for scanning, but case studies create credibility. A strong case study should include the architecture, data flow, failure modes, and tradeoffs. For example, a RAG project should explain retrieval, reranking, citation grounding, and how hallucinations were handled.

The direction

This site is moving toward a more useful system: projects, posts, profile content, and proof assets should stay structured while the public portfolio remains fast, readable, and honest.